Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Bayesian Robust Optimization for Imitation Learning
Authors: Daniel Brown, Scott Niekum, Marek Petrik
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,387 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,092 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results show that BROIL provides a natural way to interpolate between return-maximizing and risk-minimizing behaviors and outperforms existing risk-sensitive and risk-neutral inverse reinforcement learning algorithms. In the next two sections we explore two case studies that highlight the performance and benefits of using BROIL for robust policy optimization. We sampled 2000 reward functions from the prior distributions over costs and computed the CVa R optimal policy with α = 0.99 for different values of λ. Figure 5 shows that both formulations of BROIL significantly outperform Max Ent IRL and LPAL. |
| Researcher Affiliation | Academia | Daniel S. Brown UC Berkeley EMAIL Scott Niekum University of Texas at Austin EMAIL Marek Petrik University of New Hampshire EMAIL |
| Pseudocode | No | The paper includes mathematical formulations but does not contain structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code to reproduce experiments is available at https://github.com/dsbrown1331/broil |
| Open Datasets | No | The paper describes generating samples or using a single demonstration, but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper mentions running experiments on "a personal laptop" but does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | We sampled 2000 reward functions from the prior distributions over costs and computed the CVa R optimal policy with α = 0.99 for different values of λ. Given the single demonstration, we generated 2000 samples from the posterior P(R | D) using Bayesian IRL [46]. We used a relatively small inverse temperature parameter (β = 10). |